High‐Lactate‐Metabolizing Photosynthetic Bacteria Reprogram Tumor Immune Microenvironment
Bibliographic record
Abstract
The elevated levels of lactate in tumor tissue play a pivotal role in fostering an immunosuppressive microenvironment. Therefore, efficiently reducing lactate levels to reprogram tumor immune microenvironment (TIM) is considered a crucial step for boosted immunotherapy. Here, a high-lactate-metabolizing photosynthetic bacteria (LAB-1) is selectively screened for TIM reprogramming, which then improves the efficacy of tumor immunotherapy. The culture medium for LAB-1 screening is initially developed through an orthogonal experiment, simulating the tumor microenvironment (TME) and utilizing lactate as the sole organic carbon source. As demonstrated in a murine 4T1 model, LAB-1 colonizes the TME selectively, resulting in a significant reduction in lactate levels and a subsequent increase in pH values within the tumor tissue. Furthermore, single-cell RNA sequencing analysis reveals that LAB-1 effectively reprograms the TIM, thereby enhancing the effectiveness of antitumor immune therapy. This approach of utilizing lactate-consuming bacteria represents a potent tool for augmenting tumor immunotherapy efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".